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org.nd4j.linalg.api.ops.impl.indexaccum.custom.ArgMin Maven / Gradle / Ivy
/*
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* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.indexaccum.custom;
import lombok.Data;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Collections;
import java.util.List;
import java.util.Map;
@Data
public class ArgMin extends DynamicCustomOp {
protected boolean keepDims = false;
private int[] dimensions;
protected DataType outputType = DataType.INT64;
public ArgMin(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v);
this.keepDims = keepDims;
this.dimensions = dimensions;
if (dimensions != null && dimensions.length > 0)
addIArgument(dimensions);
addBArgument(keepDims);
addDArgument(outputType);
}
public ArgMin() {
}
public ArgMin(INDArray x, INDArray z, boolean keepDims, int... dimensions) {
super(new INDArray[]{x}, z != null ? new INDArray[] {z} : new INDArray[0]);
this.keepDims = keepDims;
this.dimensions = dimensions;
if (dimensions != null && dimensions.length > 0)
addIArgument(dimensions);
addBArgument(keepDims);
addDArgument(outputType);
}
public ArgMin(INDArray x, INDArray z, int... dimensions) {
this(x, z, false, dimensions);
}
public ArgMin(INDArray x, int... dimensions) {
this(x, null, dimensions);
}
public ArgMin(INDArray x, boolean keepDims, int... dimensions) {
this(x, null, keepDims, dimensions);
}
@Override
public String opName() {
return "argmin";
}
@Override
public String tensorflowName() {
return "ArgMin";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if(attributesForNode.containsKey("output_type")) {
outputType = TFGraphMapper.convertType(attributesForNode.get("output_type").getType());
} else {
outputType = DataType.LONG;
}
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && (inputDataTypes.size() == 1 || inputDataTypes.size() == 2),
"Expected 1 or 2 input datatype to argmax, got %s", inputDataTypes); //2nd input: axis
//TODO make this output datatype configurable! (long/int)
return Collections.singletonList(outputType == null ? DataType.LONG : outputType);
}
}